FAST addresses sampling inefficiency in autonomous driving reinforcement learning by introducing Dynamic Parallel Sampling Alignment to decouple sampling loops from individual episode terminations. It achieves up to 1.78 times wall-clock speedup over single-clip baselines while maintaining statistical unbiasedness through Scaled Mask-Padding Optimization.
FAST: A Framework for Aligned Sampling and Training in Parallel Reinforcement Learning
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